1998～2016年中国地级以上城市PM_(2.5)污染时空格局
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摘要
利用1998～2016年全球PM_(2.5)浓度栅格数据集,以地级以上城市为基本单元提取出PM_(2.5)浓度数据,采用核密度估计法、全局空间自相关、局部空间自相关、热点分析等方法探讨我国地级以上城市PM_(2.5)污染的时空格局演化规律.结果显示:(1)研究期内我国PM_(2.5)浓度总体呈现上升趋势,年均增长0.55μg/m~3.变化趋势可以分为2个阶段:1998～2007年呈快速增长态势;2008～2016年呈现"下降～增长～下降"的变化趋势.按地区分析,东部和中部地区呈现相似的变化趋势.西部地区和东北地区均整体呈现增长的态势,但西部地区变化较为平缓,东北地区波动较为剧烈.(2)研究期内核密度曲线峰值逐步右移,这表明中国地级以上城市PM_(2.5)污染程度总体上在加剧,且东部和中部城市加剧程度远大于西部.(3)PM_(2.5)污染在空间分布上具有显著的空间正相关特征.高值聚集区集中分布在山东、河南、河北、江苏、安徽、湖南、湖北的大部分地区以及四川东部地区,1998～2007年间高值聚集城市数量呈现增加的态势,2007年达到峰值,空间上表现为向西部和南部扩张;此后高值集聚城市数量逐渐减少,聚集区南界逐渐北移.低值聚集区集中分布在内蒙古、黑龙江西北部、新疆、西藏、台湾、海南、福建等地区.研究期内低值聚集区城市数量整体呈现先增加后减少的波动状态.
The spatial-temporal pattern of PM_(2.5) in Chinese prefecture-level cities over 1998～2016 has been explored in this study based on Kernel Density Estimation(KDE), Global Spatial Autocorrelation, Local Spatial Autocorrelation, Hotspot Analysis and other methods with the data of PM_(2.5) concentration extracted from the 1998 to 2016 global PM_(2.5) concentration raster dataset at prefecture-level city. The results showed that first the concentration of PM_(2.5) in China has been rising with an average annual increase of 0.55μg/m~3 as a whole from 1998 to 2016. Its changing trend could be divided into two stages: rapid growth from 1998 to 2007 and decline-growth-decline fluctuations from 2008 to 2016. Geographically, eastern China and central China experienced similar changing trends, while western China experienced a moderate changing trend, and the changing trend of northeastern China was more dramatic. Second, from 1998 to 2016, the peak of the core density curve gradually shifted to the right, indicating that PM_(2.5) pollution in Chinese prefecture-level cities was generally increasing. The shifting was far more substantial in eastern and central China than in western China. Third, PM_(2.5) pollution presented a significant positive spatial correlation. The high-value clusters were concentrated in most parts of Shandong, Henan, Hebei, Jiangsu, Anhui, Hunan, Hubei, and eastern Sichuan. The number of high-value cluster cities had increased from 1998 to 2007 with the peak achieved in 2007. The high-value areas westward and southward during this period. After 2007, the number of high-value agglomerated cities had gradually decreased, and its southern boundary gradually moved northward. Low-value city clusters were concentrated in Inner Mongolia, northwestern Heilongjiang, Xinjiang, Tibet, Taiwan, Hainan, Fujian and other regions, and the number of low-value cluster cities showed an increase first and then decreased trend with annual variations.
The spatial-temporal pattern of PM_(2.5) in Chinese prefecture-level cities over 1998～2016 has been explored in this study based on Kernel Density Estimation(KDE), Global Spatial Autocorrelation, Local Spatial Autocorrelation, Hotspot Analysis and other methods with the data of PM_(2.5) concentration extracted from the 1998 to 2016 global PM_(2.5) concentration raster dataset at prefecture-level city. The results showed that first the concentration of PM_(2.5) in China has been rising with an average annual increase of 0.55μg/m~3 as a whole from 1998 to 2016. Its changing trend could be divided into two stages: rapid growth from 1998 to 2007 and decline-growth-decline fluctuations from 2008 to 2016. Geographically, eastern China and central China experienced similar changing trends, while western China experienced a moderate changing trend, and the changing trend of northeastern China was more dramatic. Second, from 1998 to 2016, the peak of the core density curve gradually shifted to the right, indicating that PM_(2.5) pollution in Chinese prefecture-level cities was generally increasing. The shifting was far more substantial in eastern and central China than in western China. Third, PM_(2.5) pollution presented a significant positive spatial correlation. The high-value clusters were concentrated in most parts of Shandong, Henan, Hebei, Jiangsu, Anhui, Hunan, Hubei, and eastern Sichuan. The number of high-value cluster cities had increased from 1998 to 2007 with the peak achieved in 2007. The high-value areas westward and southward during this period. After 2007, the number of high-value agglomerated cities had gradually decreased, and its southern boundary gradually moved northward. Low-value city clusters were concentrated in Inner Mongolia, northwestern Heilongjiang, Xinjiang, Tibet, Taiwan, Hainan, Fujian and other regions, and the number of low-value cluster cities showed an increase first and then decreased trend with annual variations.
引文
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1按照国家统计局的划分方法,东部地区包括北京、天津、河北、上海、江苏、浙江、福建、山东、广东、海南10个省(市);中部地区包括山西、安徽、江西、河南、湖北、湖南6个省;西部地区:包括内蒙古、广西、重庆、四川、贵州、云南、西藏、陕西、甘肃、青海、宁夏、新疆12个省(市、自治区);东北地区:包括辽宁、吉林、黑龙江3个省.
[2]王艳琴.GB 3095-2012《环境空气质量标准》将分期实施[J].中国标准导报,2012,27(4):4-5.Wang Y Q. GB 3095-2012"Environmental air quality standards"will be implemented in phases[J]. China Quality and Standards Review,2012,27(4):4-5.
[3]穆泉,张世秋.中国2001~2013年PM2.5重污染的历史变化与健康影响的经济损失评估[J].北京大学学报(自然科学版),2015,51(4):694-706.Mu Q, Zhang S Q. Assessment of the trend of heavy PM2.5 pollution days and economic loss of health effects during 2001-2013[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2015,51(4):694-706.
[4]孙建如,钟韵.我国大城市PM2.5影响因素的经济分析——基于市级面板数据的实证研究[J].生态经济,2015,31(3):62-65+77.Sun J R, Zhong Y. Economic analysis on the factors infl uencing PM2.5in China's metropolises:An empirical study base on city-level panel data[J]. Ecological Economy,2015,31(3):62-65+77.
[5]王占山,李云婷,陈添,等.2013年北京市PM2.5的时空分布[J].地理学报,2015,70(1):110-120.Wang Z S,Li Y T,Chen T,et al. Spatial-temporal characteristics of PM2.5 in Beijing in 2013[J]. Acta Geographica Sinica, 2015,70(1):110-120.
[6]许珊,邹滨,蒲强,等.土地利用/覆盖的空气污染效应分析[J].地球信息科学学报,2015,17(3):290-299.Xu S, Zou B, Pu Q, et al. Impact Analysis of land use/cover on air pollution[J]. Journal of Geo-Information Science, 2015,17(3):290-299.
[7]张莹,赵燕.珠三角区域PM2.5浓度特征及时空变化规律[J].科技与创新,2017,5(13):138-139.Zhang Y,Zhao Y. Characteristics of PM2.5 concentration and time-space change in the Pearl River Delta Region[J]. Science and Technology&Innovation, 2017,5(13):138-139.
[8]王振波,方创琳,许光,等.2014年中国城市PM2.5浓度的时空变化规律[J].地理学报,2015,70(11):1720-1734.Wang Z B, Fang C L, Xu G et al. Spatial-temporal characteristics of the PM2.5 in China in 2014[J]. Acta Geographica Sinica,2015,70(11):1720-1734.
[9]李沈鑫,邹滨,刘兴权,等.2013~2015年中国PM2.5污染状况时空变化[J].环境科学究,2017,30(5):678-687.Li S X, Zou B, Liu X Q, et al. Pollution status and spatial-temporal variations of PM2.5 in China during 2013-2015. Research of Environmental Sciences, 2017,30(5):678-687.
[10]周亮,周成虎,杨帆,等.2000~2011年中国PM2.5时空演化特征及驱动因素解析[J].地理学报,2017,72(11):2079-2092.Zhou L, Zhou C H, Yang F, et al. Spatio-temporal evolution and the influencing factors of PM2.5 in China between 2000 and 2011[J]. Acta Geographica Sinica, 2017,72(11):2079-2092.
[11]匡兵,卢新海,周敏,等.中国地级以上城市土地经济密度差异的时空演化分析[J].地理科学,2017,37(12):1850-1858.Kuang B, Lu X H, Zhou M, et al. Analysis of spatial-temporal evolution of urban land economic density disparity in prefecture-level cities of China[J]. Scientia Geographica Sinica, 2017,37(12):1850-1858.
[12]徐玉琴,张扬,戴志辉.基于非参数核密度估计和Copula函数的配电网供电可靠性预测[J].华北电力大学学报(自然科学版),2017,44(6):14-19.Xu Y Q, Zhang Y, Dai Z H. Power supply reliability prediction of distribution network based on nonparametric kernel density estimation and copula function[J]. Journal of North China Electric Power University(Natural Science Edition), 2017,44(6):14-19.
[13]卢敏,杨柳,王金茵,等.基于核密度估计的点群密度制图应用研究[J].测绘工程,2017,26(4):70-74+80.Lu M, Yang L, Wang J Y, et al. Applications of pointgroup density cartography based on kernel density estimation[J]. Engineering of Surveying and Mapping, 2017,26(4):70-74+80.
[14]王泽宇,郭萌雨,孙才志,等.基于可变模糊识别模型的现代海洋产业发展水平评价[J].资源科学,2015,37(3):534-545.Wang Z Y, Guo M Y, Sun C Z, et al. The evaluation of modern marine industry development levels[J]. Resources Science, 2015,37(3):534-545.
[15]曹景林,邰凌楠.基于消费视角的我国中等收入群体人口分布及变动测度[J].广东财经大学学报,2015,30(6):4-15.Cao J L, Tai L N. The Measurement of distribution and Change of the middle-income group in China from the perspective of consumption[J]. Journal of Guangdong University of Finance&Economics,2015,30(6):4-15.
[16]潘竟虎,尹君.中国地级及以上城市发展效率差异的DEAESDA测度[J].经济地理,2012,32(12):53-60.Pan J H, Yin J. Analysis on the urban development efficiency of cities at prefecture level or above in China based on DEA-ESDA[J].Economic Geography, 2012,32(12):53-60.
[17]王红亮,胡伟平,吴驰.空间权重矩阵对空间自相关的影响分析——以湖南省城乡收入差距为例[J].华南师范大学学报(自然科学版),2010,63(1):110-115.Wang H L, Hu W P, Wu C. Analyzing the effect of spatial weighed matrix on spatial autocorrelation ation-taking hunan's income GAP between urban and rural areas as a case[J]. Journal of South ChinaNormal University(Natural Science Edition),2010,63(1):110-115.
[18]吴珣,杨婕,张红.不同空间权重定义下中国人口分布空间自相关特征分析[J].地理信息世界,2017,24(2):32-38.Wu X, Yang J, Zhang H. Analyzing spatial autocorrelation of population distribution in different spatial weights:a case of China[J].Geomatics World, 2017,24(2):32-38.
[19] Anselin L.Local Indicators of Spatial Association—LISA[J].Geographical Analysis,1995,27(2):93-115.
[20]罗毅,邓琼飞,杨昆,等.近20年来中国典型区域PM2.5时空演变过程[J].环境科学,2018,43(7):1-13.Luo Y, Deng Q F, Yang K, et al. Spatial-temporal change evolution of PM2.5 in typical regions of China in recent 20years[J]. Environmental Science, 2018,43(7):1-13.
[21]周磊,武建军,贾瑞静,等.京津冀PM2.5时空分布特征及其污染风险因素[J].环境科学研究,2016,29(4):483-493.Zhou W, Wu J J, Jia R J, et al. Investigation of temporal-spatial characteristics and underlying risk factors of PM2.5 pollution in Beijing-Tianjin-Hebei area[J]. Research of Environmental Sciences,2016,29(4):483-493.
[22]张殷俊,陈曦,谢高地,等.中国细颗粒物(PM2.5)污染状况和空间分布[J].资源科学,2015,37(7):1339-1346.Zhang Y J, Chen X, Xie G D, et al. Pollution status and spatial distribution of PM2.5 in China[J]. Resources Science, 2015,37(7):1339-1346.
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1按照国家统计局的划分方法,东部地区包括北京、天津、河北、上海、江苏、浙江、福建、山东、广东、海南10个省(市);中部地区包括山西、安徽、江西、河南、湖北、湖南6个省;西部地区:包括内蒙古、广西、重庆、四川、贵州、云南、西藏、陕西、甘肃、青海、宁夏、新疆12个省(市、自治区);东北地区:包括辽宁、吉林、黑龙江3个省.
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